clean script

examples/mnist-pytorch-fedprox/client/entrypoint

@@ -146,13 +146,8 @@ def train(in_model_path, out_model_path, data_path=None, batch_size=32, epochs=1
 
     mu = client_settings['mu']
 
-    print("mu: ", mu)
+    print("mu value: ", mu)
 
-    print("data_path: ", data_path)
-    print(os.getcwd())
-    print("list data path: ", os.listdir('/var/data'))
-
-    print("list data/clients path: ", os.listdir('/var/data/clients'))
     # Load data
     x_train, y_train = load_data(data_path)
 
@@ -176,12 +171,8 @@ def train(in_model_path, out_model_path, data_path=None, batch_size=32, epochs=1
             proximal_term = 0.0
             for w, w_t in zip(model.parameters(), global_model.parameters()):
                 proximal_term += (w - w_t).norm(2)
-            #print("proximal_term: ", proximal_term)
-
-            # loss = criterion(outputs, batch_y)  # <-- old
 
-            # loss = loss_function(y_pred, label) + (args.mu / 2) * proximal_term <-- fed prox term
-            loss = criterion(outputs, batch_y) + (mu / 2) * proximal_term  # <-- new
+            loss = criterion(outputs, batch_y) + (mu / 2) * proximal_term 
 
             loss.backward()
             optimizer.step()